
import pandas as pd
import numpy as np
import math
import scipy
from scipy.stats.stats import pearsonr

import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

import plotly.io as pio

import datetime
import matplotlib.dates as mdates


# File path
file_path = 'data/road safety germany.csv'

# Open File
df_road_safety_germany = pd.read_csv(file_path,  delimiter=';')

# Adjust Data

# Step 1: This file has the columns Year (YYYY) & Month (mmm)(de), for convience we will add a date column as the table index. 
#         This column will contain the date of the first day of the corresponding month.
# - Step 1.1: Convert the month values (mmm)(de) (e.i. Januar, Februar, März, ..) to month (mm) values (i.e. 01, 02, 03, ..)
df_road_safety_germany = df_road_safety_germany.replace(['Januar', 'Februar', 'März', 'April', 'Mai', 'Juni', 'Juli', 
                                                         'August', 'September', 'Oktober', 'November', 'Dezember'], 
                                                        ['01','02','03','04','05','06','07','08','09','10','11','12'])

# - Step 1.2: Add [Date] column to dataframe
df_road_safety_germany['Date'] = '01-' + df_road_safety_germany['Month'] + '-' + df_road_safety_germany['Year'].astype(str)
# - Convert string to date value
df_road_safety_germany['Date'] = pd.to_datetime(df_road_safety_germany['Date'], format = '%d-%m-%Y')
# - Set date column as index 
df_road_safety_germany.set_index('Date', inplace=True)

# Step 2: Clean Data, this dataframe contains '...' values for future dates since there is no data available yet.
#         These values will be replaced by empty values 
df_road_safety_germany = df_road_safety_germany.replace({'...': None})



# Step 3: Select only the necesarry columns from the dataframe - We're only interested in the data relating to human injury
df_road_safety_germany = df_road_safety_germany[['Unfälle mit Personenschaden - Insgesamt', 
                                                'Schwerwiegende Unfälle mit Sachschaden i.e.S - Insgesamt',
                                                'Sonst. Unfälle unter dem Einfluss berausch. Mittel - Insgesamt',
                                                'Übrige Sachschadensunfälle - Insgesamt', 
                                                'Insgesamt - Insgesamt']]

# Step 4: Rename column names from German to English
df_road_safety_germany = df_road_safety_germany.rename(columns={
    'Unfälle mit Personenschaden - Insgesamt': 'Accidents involving human injury', 
    'Schwerwiegende Unfälle mit Sachschaden i.e.S - Insgesamt': 'Serious accidents with material damange',
    'Sonst. Unfälle unter dem Einfluss berausch. Mittel - Insgesamt': 'Accidents under the influence of toxins', 
    'Übrige Sachschadensunfälle - Insgesamt': 'Other accidents',
    'Insgesamt - Insgesamt': 'Total accidents'})

# Step 5: Convert to int
#         First drop future dates without values
df_road_safety_germany = df_road_safety_germany.dropna()
df_road_safety_germany = df_road_safety_germany.astype({'Accidents involving human injury': 'int64'})

# Read File
df_road_safety_germany


# File path
file_path = 'data/road safety germany.csv'
# Open File
df_accidents_human_injury = pd.read_csv(file_path,  delimiter=';')

# Adjust Data

# Step 1: This file has the columns Year (YYYY) & Month (mmm)(de), for convience we will add a date column as the table index. 
#         This column will contain the date of the first day of the corresponding month.
# - Step 1.1: Convert the month values (mmm)(de) (e.i. Januar, Februar, März, ..) to month (mm) values (i.e. 01, 02, 03, ..)
df_accidents_human_injury = df_accidents_human_injury.replace(['Januar', 'Februar', 'März', 'April', 'Mai', 'Juni', 'Juli', 
                                                         'August', 'September', 'Oktober', 'November', 'Dezember'], 
                                                        ['01','02','03','04','05','06','07','08','09','10','11','12'])

# - Step 1.2: Add [Date] column to dataframe
df_accidents_human_injury['Date'] = '01-' + df_accidents_human_injury['Month'] + '-' + df_accidents_human_injury['Year'].astype(str)
# - Convert string to date value
df_accidents_human_injury['Date'] = pd.to_datetime(df_accidents_human_injury['Date'], format = '%d-%m-%Y')
# - Set date column as index 
df_accidents_human_injury.set_index('Date', inplace=True)

# Step 2: Clean Data, this dataframe contains '...' values for future dates since there is no data available yet.
#         These values will be replaced by empty values 
df_accidents_human_injury = df_accidents_human_injury.replace({'...': None})



# Step 3: Select only the necesarry columns from the dataframe - We're only interested in the data relating to human injury
df_accidents_human_injury = df_accidents_human_injury[['Unfälle mit Personenschaden - innerorts', 
                                                       'Unfälle mit Personenschaden - außerorts (ohne Autobahnen)',
                                                       'Unfälle mit Personenschaden - auf Autobahnen',
                                                       'Unfälle mit Personenschaden - Insgesamt', 
                                                      ]]

# Step 4: Rename column names from German to English. 
df_accidents_human_injury = df_accidents_human_injury.rename(columns={
    'Unfälle mit Personenschaden - Insgesamt': 'Accidents involving human injury - total', 
    'Unfälle mit Personenschaden - innerorts': 'Accidents involving human injury - within city limits',
    'Unfälle mit Personenschaden - auf Autobahnen': 'Accidents involving human injury - highway', 
    'Unfälle mit Personenschaden - außerorts (ohne Autobahnen)': 'Accidents involving human injury - outside city limits, off highway',
    })


# Step 5: Convert to int
#         First drop future dates without values
df_accidents_human_injury = df_accidents_human_injury.dropna()
df_accidents_human_injury = df_accidents_human_injury.astype({'Accidents involving human injury - total': 'int64'})
df_accidents_human_injury = df_accidents_human_injury.astype({'Accidents involving human injury - within city limits': 'int64'})
df_accidents_human_injury = df_accidents_human_injury.astype({'Accidents involving human injury - highway': 'int64'})
df_accidents_human_injury = df_accidents_human_injury.astype({'Accidents involving human injury - outside city limits, off highway': 'int64'})

# Read File
df_accidents_human_injury


# Pipeline Covid Positive Test

# File path
file_path = 'data/positive covid tests germany.csv'

# Open File
df_positive_covid_test_germany = pd.read_csv(file_path,  delimiter=';')

# Adjust Data
# Step 1: This file has the column Year_Week (YYYY_ww) for convience we will add a date column as the table index. 
#         This column will contain the date of the first day of the corresponding week.
# - Step 1.1: create date column
df_positive_covid_test_germany['Date'] = None
# - Step 1.1: Convert the year and week data to dates using the strptime function. 
#             For the year 2020 the week needs to be offset by 7 days, this is due to different interpretations of a 53th 
#             week in 2019.
for i in range(len(df_positive_covid_test_germany)):
    d = df_positive_covid_test_germany['Year_Week'][i]
    if d.startswith('2020'):
        df_positive_covid_test_germany['Date'][i] = datetime.datetime.strptime(d + '-1', "%Y_%W-%w") - datetime.timedelta(days=7)
    elif d.startswith('2021'):
        df_positive_covid_test_germany['Date'][i] = datetime.datetime.strptime(d + '-1', "%Y_%W-%w")
    else:
        df_positive_covid_test_germany['Date'][i] = datetime.datetime.strptime(d + '-1', "%Y_%W-%w")

# - Set date column as index 
df_positive_covid_test_germany.set_index('Date', inplace=True)

# Step 2: Clean Data



# Step 3: Select only the necesarry columns from the dataframe 
df_positive_covid_test_germany = df_positive_covid_test_germany[['Gesamt']]

# Step 4: Rename column names from German to English
df_positive_covid_test_germany = df_positive_covid_test_germany.rename(columns={
    'Gesamt': 'Number of Positive COVID-19 tests per 100.000 capita'})

# Step 5: Convert the decimal comma to a decimal point, then convert the string to a float, 
df_positive_covid_test_germany["Number of Positive COVID-19 tests per 100.000 capita"]=df_positive_covid_test_germany["Number of Positive COVID-19 tests per 100.000 capita"].str.replace(',','.')
df_positive_covid_test_germany['Number of Positive COVID-19 tests per 100.000 capita'] = df_positive_covid_test_germany['Number of Positive COVID-19 tests per 100.000 capita'].astype(float)

# Read File
df_positive_covid_test_germany


# Pipeline Covid Deaths

# File path
file_path = 'data/covid deaths germany.csv'

# Open File
df_covid_deaths_germany = pd.read_csv(file_path,  delimiter=';')

# Adjust Data
# Step 1: This file has the column Year_Month (YYYY-mm), for convience we will add a date column as the table index. 
#         This column will contain the date of the first day of the corresponding month.
df_covid_deaths_germany['Date'] = df_covid_deaths_germany['Year-Month'] + '-01'
# - Convert string to date value
df_covid_deaths_germany['Date'] = pd.to_datetime(df_covid_deaths_germany['Date'], format = '%Y-%m-%d')
# - Set date column as index 
df_covid_deaths_germany.set_index('Date', inplace=True)

# Step 2: Clean Data

# Step 3: Select only the necesarry columns from the dataframe 
df_covid_deaths_germany = df_covid_deaths_germany[['Number of Covid Deaths']]

# Step 4: Rename column names - not necessary

# Read File
df_covid_deaths_germany


# Pipeline Covid Intensive Care Cases

# File path
file_path = 'data/intensive care covid cases germany.csv'

# Open File
df_ic_cases_covid_germany = pd.read_csv(file_path,  delimiter=',')


# Adjust Data
# Step 1: Convert date data from datetime to date
for i in range(len(df_ic_cases_covid_germany)):
    d = df_ic_cases_covid_germany['date'][i] 
    df_ic_cases_covid_germany['date'][i] = datetime.datetime.strptime(d, '%Y-%m-%dT%H:%M:%S%z').date()


# Step 2: Clean Data

# Step 3: Select only the necesarry columns from the dataframe 

# Step 4: Rename column names
df_ic_cases_covid_germany = df_ic_cases_covid_germany.rename(columns={
    'date': 'Date',
    'COVID-19-Fälle': 'Covid Cases on IC'})

df_ic_cases_covid_germany.set_index('Date', inplace=True)
# Read File
df_ic_cases_covid_germany

# ------------ Remark ---------------
# I have no idea why this error is showing, but is the only way I can convert the datetime value to a date value
# ERROR: 
    # A value is trying to be set on a copy of a slice from a DataFrame
    #
    # See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
    # df_ic_cases_covid_germany['date'][i] = datetime.strptime(d, '%Y-%m-%dT%H:%M:%S%z').date()

C:\Users\larsk\AppData\Local\Temp\ipykernel_27300\3601102438.py:14: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_ic_cases_covid_germany['date'][i] = datetime.datetime.strptime(d, '%Y-%m-%dT%H:%M:%S%z').date()


# File path
file_path = 'traffic data output/traffic data out.csv'

# Open File
df_traffic_data = pd.read_csv(file_path,  delimiter=',')

# Step 4: Rename column names
df_traffic_data = df_traffic_data.rename(columns={
    'Datum': 'Date',
    'Autobahn_KFZ_Total': 'Highway_Vehicle_Count', 
    'Autobahn_KFZ_Total_Rolling_Average_7_Days': 'Highway_Vehicle_Count_Moving_Average_7_Days', 
    'Bundesstrassen_KFZ_Total': 'Federal_Road_Vehicle_Count', 
    'Bundesstrassen_KFZ_Total_Rolling_Average_7_Days': 'Federal_Road_Vehicle_Count_Moving_Average_7_Days', 
    'Total': 'Total_Vehicle_Count', 
    'Total_Rolling_Average_7_Days': 'Total_Vehicle_Count_Moving_Average_7_Days'
})


# Set index
df_traffic_data.set_index('Date', inplace=True)


# Read File
df_traffic_data


fig = px.line(df_traffic_data, x=df_traffic_data.index, 
              y= ['Highway_Vehicle_Count_Moving_Average_7_Days', 
                  'Federal_Road_Vehicle_Count_Moving_Average_7_Days',
                  'Total_Vehicle_Count_Moving_Average_7_Days'],
              title = "Figure 1: Road usage in Germany 2017-2021")

fig.update_yaxes(range=[0, 70000000])

fig.show()


# Step 1: select data
df1 = df_road_safety_germany

#Step 2: create line-plot
fig = px.line(df1, x= df_road_safety_germany.index, y= "Accidents involving human injury",
             title = "Figure 2: Weekly total accidents traffic accidents Germany")
fig.show()


# Step 1: select data
df2 = df_accidents_human_injury

# Step 2: Sum up all the different categories of incidents involving human injuries
total_withincitylimits = 0
for i in df_accidents_human_injury['Accidents involving human injury - within city limits']:
    total_withincitylimits = total_withincitylimits + i

total_highway = 0
for i in df_accidents_human_injury['Accidents involving human injury - highway']:
    total_highway = total_highway + i

total_outsidecitylimits = 0
for i in df_accidents_human_injury['Accidents involving human injury - outside city limits, off highway']:
    total_outsidecitylimits = total_outsidecitylimits + i

#Step 3: Create bar-plot
categories = ['total_withincitylimits', 'total_highway', 'total_outsidecitylimits']
accidents = [total_withincitylimits, total_highway, total_outsidecitylimits]

fig = px.bar(x = categories, y = accidents, 
             title = "Figure 3: Traffic Accidents With Human Injury per Category 2011-2022")

fig.update_xaxes(title_text="Accident Category")
fig.update_yaxes(title_text="Number of Accidents")

fig.show()


# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces
fig.add_trace(
    go.Line(x = df_traffic_data.index, 
           y = df_traffic_data['Total_Vehicle_Count_Moving_Average_7_Days'], 
           name = 'Total Vehicle Count Moving Average 7 Days', 
            line_color = 'blue'),
    secondary_y=True,
)

fig.add_trace(
    go.Line(x = df_road_safety_germany.index, 
            y = df_road_safety_germany['Accidents involving human injury'], 
            name = 'Accidents involving human injury',
            line_color = 'green'),
    secondary_y=False,
)
# Add figure title
fig.update_layout(
    title_text="Figure 4: Road usage vs. Road safety Analysis"
)

# Set x-axis title
fig.update_xaxes(title_text="Date")

# Set y-axes titles
fig.update_yaxes(title_text="<b>Safety Accidents</b>", secondary_y=False)
fig.update_yaxes(range=[0,35000], secondary_y=False)
fig.update_yaxes(title_text="<b>Vehicle Count</b>", secondary_y=True)
fig.update_yaxes(range=[0,70000000], secondary_y=True)
fig.update_xaxes(range=[datetime.date(2017, 1, 7), datetime.date(2022, 10, 31)])


#fig.show()
fig.show()

C:\Users\larsk\anaconda3\envs\TIL6022\lib\site-packages\plotly\graph_objs\_deprecations.py:378: DeprecationWarning:

plotly.graph_objs.Line is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.scatter.Line
  - plotly.graph_objs.layout.shape.Line
  - etc.


# Step 1: Select data 
df2 = df_positive_covid_test_germany

# Step 2: Create line-plot
fig = px.line(df2, x= df_positive_covid_test_germany.index, 
              y= "Number of Positive COVID-19 tests per 100.000 capita",
              title = "Figure 5: Total weekly positive COVID-tests Germany")
fig.update_traces(line_color='#EF553B')
fig.show()


# Step 1: Select data 
df3 = df_covid_deaths_germany

# Step 2: Create line-plot
fig = px.line(df3, x= df_covid_deaths_germany.index, y= "Number of Covid Deaths", 
              title = "Figure 6: Total COVID-Deaths Germany")
fig.update_traces(line_color='#EF553B')
fig.show()


# Step 1: Select data
df4 = df_ic_cases_covid_germany

# Step 2: Create line-plot
fig = px.line(df4, x= df_ic_cases_covid_germany.index, y= "Covid Cases on IC", 
              title="Figure 7: Total COVID-cases on IC" )
fig.update_traces(line_color='#EF553B')
fig.show()


# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces
fig.add_trace(
    go.Bar(x = df2.index, 
           y = df2['Number of Positive COVID-19 tests per 100.000 capita'], 
           name = 'Positive tests previous 7 days per 100.000 capita', 
           marker_color = 'blue',
           opacity = 0.6),
    secondary_y=True,
)

fig.add_trace(
    go.Bar(x = df3.index, 
           y = df3['Number of Covid Deaths'], 
           name = 'Monthly Covid Deaths Germany', 
           marker_color = 'red',
           opacity = 0.6),
    secondary_y=False,
)

fig.add_trace(
    go.Line(x = df4.index, 
            y = df4['Covid Cases on IC'], 
            name = 'Daily Covid IC cases Germany',
            line_color = 'black'),
    secondary_y=False,
)
# Add figure title
fig.update_layout(
    title_text="Figure 8: Complete Covid Analysis"
)

# Set x-axis title
fig.update_xaxes(title_text="Date")

# Set y-axes titles
fig.update_yaxes(title_text="<b>Covid Deaths & IC Cases</b>", secondary_y=False)
fig.update_yaxes(range=[0,25000], secondary_y=False)
fig.update_yaxes(title_text="<b>Covid Tests</b>", secondary_y=True)
fig.update_yaxes(range=[0,2000], secondary_y=True)

fig.show()

C:\Users\larsk\anaconda3\envs\TIL6022\lib\site-packages\plotly\graph_objs\_deprecations.py:378: DeprecationWarning:

plotly.graph_objs.Line is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.scatter.Line
  - plotly.graph_objs.layout.shape.Line
  - etc.


# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces
fig.add_trace(
    go.Line(x = df_traffic_data.index, 
           y = df_traffic_data['Total_Vehicle_Count_Moving_Average_7_Days'], 
           name = 'Total Vehicle Count Moving Average 7 Days', 
            line_color = 'blue'),
    secondary_y=True,
)

fig.add_trace(
    go.Line(x = df_ic_cases_covid_germany.index, 
            y = df_ic_cases_covid_germany['Covid Cases on IC'], 
            name = 'Covid Cases on IC',
            line_color = 'green'),
    secondary_y=False,
)
# Add figure title
fig.update_layout(
    title_text="Figure 9: Road usage vs. Covid-19 Analysis"
)

# Set x-axis title
fig.update_xaxes(title_text="Date")

# Set y-axes titles
fig.update_yaxes(title_text="<b>Total COVID-cases on IC</b>", secondary_y=False)
fig.update_yaxes(range=[0,6000], secondary_y=False)
fig.update_yaxes(title_text="<b>Vehicle Count</b>", secondary_y=True)
fig.update_yaxes(range=[0,70000000], secondary_y=True)
fig.update_xaxes(range=[datetime.date(2017, 1, 7), datetime.date(2022, 10, 31)])


fig.show()


#Step 1: 
# - Define two lists:
#    - Names of events
#    - Corresponding dates of events
names = ['First Case China [1]','First Case Germany [1]',
         'First big event cancelled [1]','1st lockdown begins [4]',
         'First ease of measures [1]','Test labs overloaded [1]',
         'New measures [4]','Start Lockdown light [4]','Lockdown tightened [4]',
         'Hard lockdown [4]','Start vaccination campagne [4]',
         'First big ease of measures [4]','Hard lockdown [2]', 
         'No more measures if vaccinated [4]','Omikron variant [5]', 
         'Less 2G/3G measures [3]', 'End of most measures [3]']

dates = ['2019-12-31', '2020-01-27', '2020-03-04',
         '2020-03-22', '2020-04-20', '2020-08-25',
         '2020-10-14', '2020-10-28', '2020-11-25', 
         '2020-12-12', '2020-12-27', '2021-03-08',
         '2021-03-23', '2021-05-09', '2021-12-27', 
         '2022-02-01', '2022-03-20']

# Step 2:
# - Convert date strings (e.g. 2014-10-18) to datetime
dates = [datetime.datetime.strptime(d, "%Y-%m-%d") for d in dates]

# Step 3:
# - Choose some  levels
levels = np.tile([-5, 5, -3, 3, -1, 1],
                 int(np.ceil(len(dates)/6)))[:len(dates)]

# Step 4:
# - Create figure and plot a stem plot with the date
fig, ax = plt.subplots(figsize=(8.8, 4), constrained_layout=True)
ax.set(title="Timeline COVID events Germany")

ax.vlines(dates, 0, levels, color="tab:red", alpha=0.3)  # The vertical stems.
ax.plot(dates, np.zeros_like(dates), "-o",
        color="k", markerfacecolor="w")  # Baseline and markers on it.

# Step 5:
# - annotate lines
for d, l, r in zip(dates, levels, names):
    ax.annotate(r, xy=(d, l),
                xytext=(-3, np.sign(l)*3), textcoords="offset points",
                horizontalalignment="left",
                verticalalignment="bottom" if l > 0 else "top",
                fontsize=8)

# Step 6:
# - format xaxis with 1 month intervals
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=1))
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %Y"))
plt.setp(ax.get_xticklabels(), rotation=30, ha="right", size = 8)

# Step 7:
# - remove y axis and spines
# - set margins
ax.yaxis.set_visible(False)
ax.spines[["left", "top", "right"]].set_visible(False)
ax.margins(y=0.2)

plt.show()

	Accidents involving human injury	Serious accidents with material damange	Accidents under the influence of toxins	Other accidents	Total accidents
Date
2011-01-01	16448	7045	1142	151332	175967
2011-02-01	16227	6138	1043	137738	161146
2011-03-01	21569	5919	1114	154508	183110
2011-04-01	26411	5717	1245	156631	190004
2011-05-01	30831	6099	1291	170684	208905
...	...	...	...	...	...
2022-01-01	16819	5731	1118	152944	176612
2022-02-01	16067	4889	1110	148594	170660
2022-03-01	21398	4457	1184	164299	191338
2022-04-01	20758	5082	1206	168157	195203
2022-05-01	27159	4397	1139	181329	214024

	Accidents involving human injury - within city limits	Accidents involving human injury - outside city limits, off highway	Accidents involving human injury - highway	Accidents involving human injury - total
Date
2011-01-01	10351	4874	1223	16448
2011-02-01	10791	4296	1140	16227
2011-03-01	15055	5236	1278	21569
2011-04-01	18390	6607	1414	26411
2011-05-01	22273	7017	1541	30831
...	...	...	...	...
2022-01-01	11317	4341	1161	16819
2022-02-01	10884	4011	1172	16067
2022-03-01	15507	4776	1115	21398
2022-04-01	14280	5124	1354	20758
2022-05-01	19568	6206	1385	27159

	Number of Positive COVID-19 tests per 100.000 capita
Date
2020-03-02	1.09
2020-03-09	7.73
2020-03-16	26.91
2020-03-23	40.81
2020-03-30	43.31
...	...
2022-08-29	241.96
2022-09-05	244.93
2022-09-12	274.68
2022-09-19	343.30
2022-09-26	529.81

	Number of Covid Deaths
Date
2020-03-01	1120
2020-04-01	6069
2020-05-01	1572
2020-06-01	320
2020-07-01	135
2020-08-01	152
2020-09-01	206
2020-10-01	1480
2020-11-01	8604
2020-12-01	22035
2021-01-01	21997
2021-02-01	9803
2021-03-01	5519
2021-04-01	6572
2021-05-01	4734
2021-06-01	1089
2021-07-01	276
2021-08-01	491
2021-09-01	1605
2021-10-01	2491
2021-11-01	8095
2021-12-01	10396
2022-01-01	4520
2022-02-01	5664
2022-03-01	7485
2022-04-01	5232
2022-05-01	2247
2022-06-01	1511
2022-07-01	3294
2022-08-01	2747
2022-09-01	760

	Covid Cases on IC
Date
2020-03-20	200
2020-03-21	308
2020-03-22	364
2020-03-23	451
2020-03-24	616
...	...
2022-10-05	1294
2022-10-06	1344
2022-10-07	1366
2022-10-08	1406
2022-10-09	1453

Project Group 13¶

Research Objective¶

Contribution Statement¶

Data Used¶

Notebook Overview¶

1. Imported Libraries¶

2. Data Pipelines¶

2.1 Road Safety Data Germany¶

2.2 Incidents Resulting in Human Injury Germany¶

2.3 Positive Covid-19 Tests Germany¶

2.4 People on Intensive care (IC) Due to Covid-19 Germany¶

2.6 Traffic Data Germany¶

3. Data analysis¶

3.1 Traffic Data Germany¶

3.2 Road safety Germany¶

3.3 Road usage vs. road safety Germany¶

3.4 Covid-19 Germany¶

3.5 Road usage vs. Road safety vs. Covid-19 Germany¶

3.6 Timeline Animation of Important Stages During the Covid-19 Pandemic¶

3.6.1 References for Timeline¶

	Highway_Vehicle_Count	Highway_Vehicle_Count_Moving_Average_7_Days	Federal_Road_Vehicle_Count	Federal_Road_Vehicle_Count_Moving_Average_7_Days	Total_Vehicle_Count	Total_Vehicle_Count_Moving_Average_7_Days
Date
2017-01-01	26975627	2.697563e+07	3886847	3.886847e+06	30862474	3.086247e+07
2017-01-02	41560509	3.426807e+07	7362853	5.624850e+06	48923362	3.989292e+07
2017-01-03	39117647	3.588459e+07	7740331	6.330010e+06	46857978	4.221460e+07
2017-01-04	36828052	3.612046e+07	7537968	6.632000e+06	44366020	4.275246e+07
2017-01-05	39337185	3.676380e+07	8039175	6.913435e+06	47376360	4.367724e+07
...	...	...	...	...	...	...
2021-12-27	42884833	4.293691e+07	7923371	7.957952e+06	50808204	5.089486e+07
2021-12-28	41452599	4.184089e+07	8050999	7.596847e+06	49503598	4.943774e+07
2021-12-29	41882438	4.037421e+07	8310961	7.272411e+06	50193399	4.764662e+07
2021-12-30	43390035	3.904579e+07	8555059	7.103476e+06	51945094	4.614927e+07
2021-12-31	26765605	3.820287e+07	5568421	7.044972e+06	32334026	4.524784e+07

Project Group 13¶

Research Objective¶

Contribution Statement¶

Data Used¶

Notebook Overview¶

1. Imported Libraries¶

2. Data Pipelines¶

2.1 Road Safety Data Germany¶

2.2 Incidents Resulting in Human Injury Germany¶

2.3 Positive Covid-19 Tests Germany¶

2.4 People on Intensive care (IC) Due to Covid-19 Germany¶

2.5 COVID-Related Deaths Germany¶

2.6 Traffic Data Germany¶

3. Data analysis¶

3.1 Traffic Data Germany¶

3.2 Road safety Germany¶

3.3 Road usage vs. road safety Germany¶

3.4 Covid-19 Germany¶

3.5 Road usage vs. Road safety vs. Covid-19 Germany¶

3.6 Timeline Animation of Important Stages During the Covid-19 Pandemic¶

3.6.1 References for Timeline¶